{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 构建两层的神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import do_not_src.week_4_2.testCases\n",
    "from do_not_src.week_4_2.dnn_utils import sigmoid, sigmoid_backward, relu, relu_backward #参见资料包\n",
    "import do_not_src.week_4_2.lr_utils #参见资料包，或者在文章底部copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(n_x,n_h,n_y):\n",
    "    \"\"\"\n",
    "    此函数是为了初始化两层网络参数而使用的函数。\n",
    "    参数：\n",
    "        n_x - 输入层节点数量\n",
    "        n_h - 隐藏层节点数量\n",
    "        n_y - 输出层节点数量\n",
    "    \n",
    "    返回：\n",
    "        parameters - 包含你的参数的python字典：\n",
    "            W1 - 权重矩阵,维度为（n_h，n_x）\n",
    "            b1 - 偏向量，维度为（n_h，1）\n",
    "            W2 - 权重矩阵，维度为（n_y，n_h）\n",
    "            b2 - 偏向量，维度为（n_y，1）\n",
    "\n",
    "    \"\"\"\n",
    "    W1 = np.random.randn(n_h, n_x) * 0.01\n",
    "    b1 = np.zeros((n_h, 1))\n",
    "    W2 = np.random.randn(n_y, n_h) * 0.01\n",
    "    b2 = np.zeros((n_y, 1))\n",
    "    \n",
    "    #使用断言确保我的数据格式是正确的\n",
    "    assert(W1.shape == (n_h, n_x))\n",
    "    assert(b1.shape == (n_h, 1))\n",
    "    assert(W2.shape == (n_y, n_h))\n",
    "    assert(b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试initialize_parameters==============\n",
      "W1 = [[ 0.01624345 -0.00611756 -0.00528172]\n",
      " [-0.01072969  0.00865408 -0.02301539]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[ 0.01744812 -0.00761207]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "print(\"==============测试initialize_parameters==============\")\n",
    "parameters = initialize_parameters(3,2,1)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# L层的神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters_deep(layers_dims):\n",
    "    \"\"\"\n",
    "    此函数是为了初始化多层网络参数而使用的函数。\n",
    "    参数：\n",
    "        layers_dims - 包含我们网络中每个图层的节点数量的列表\n",
    "    \n",
    "    返回：\n",
    "        parameters - 包含参数“W1”，“b1”，...，“WL”，“bL”的字典：\n",
    "                     W1 - 权重矩阵，维度为（layers_dims [1]，layers_dims [1-1]）\n",
    "                     bl - 偏向量，维度为（layers_dims [1]，1）\n",
    "    \"\"\"\n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layers_dims)\n",
    "    \n",
    "    for l in range(1,L):\n",
    "        parameters[\"W\" + str(l)] = np.random.randn(layers_dims[l], layers_dims[l - 1]) / np.sqrt(layers_dims[l - 1])\n",
    "        parameters[\"b\" + str(l)] = np.zeros((layers_dims[l], 1))\n",
    "        \n",
    "        #确保我要的数据的格式是正确的\n",
    "        assert(parameters[\"W\" + str(l)].shape == (layers_dims[l], layers_dims[l-1]))\n",
    "        assert(parameters[\"b\" + str(l)].shape == (layers_dims[l], 1))\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试initialize_parameters_deep==============\n",
      "W1 = [[ 0.79989897  0.19521314  0.04315498 -0.83337927 -0.12405178]\n",
      " [-0.15865304 -0.03700312 -0.28040323 -0.01959608 -0.21341839]\n",
      " [-0.58757818  0.39561516  0.39413741  0.76454432  0.02237573]\n",
      " [-0.18097724 -0.24389238 -0.69160568  0.43932807 -0.49241241]]\n",
      "b1 = [[0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "W2 = [[-0.59252326 -0.10282495  0.74307418  0.11835813]\n",
      " [-0.51189257 -0.3564966   0.31262248 -0.08025668]\n",
      " [-0.38441818 -0.11501536  0.37252813  0.98805539]]\n",
      "b2 = [[0.]\n",
      " [0.]\n",
      " [0.]]\n"
     ]
    }
   ],
   "source": [
    "#测试initialize_parameters_deep\n",
    "print(\"==============测试initialize_parameters_deep==============\")\n",
    "layers_dims = [5,4,3]\n",
    "parameters = initialize_parameters_deep(layers_dims)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_forward(A,W,b):\n",
    "    \"\"\"\n",
    "    实现前向传播的线性部分。\n",
    "\n",
    "    参数：\n",
    "        A - 来自上一层（或输入数据）的激活，维度为(上一层的节点数量，示例的数量）\n",
    "        W - 权重矩阵，numpy数组，维度为（当前图层的节点数量，前一图层的节点数量）\n",
    "        b - 偏向量，numpy向量，维度为（当前图层节点数量，1）\n",
    "\n",
    "    返回：\n",
    "         Z - 激活功能的输入，也称为预激活参数\n",
    "         cache - 一个包含“A”，“W”和“b”的字典，存储这些变量以有效地计算后向传递\n",
    "    \"\"\"\n",
    "    Z = np.dot(W,A) + b\n",
    "    assert(Z.shape == (W.shape[0],A.shape[1]))\n",
    "    cache = (A,W,b)\n",
    "     \n",
    "    return Z,cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试linear_forward==============\n",
      "Z = [[ 3.26295337 -1.23429987]]\n"
     ]
    }
   ],
   "source": [
    "#测试linear_forward\n",
    "print(\"==============测试linear_forward==============\")\n",
    "\n",
    "def linear_forward_test_case():\n",
    "    np.random.seed(1)\n",
    "    \"\"\"\n",
    "    X = np.array([[-1.02387576, 1.12397796],\n",
    " [-1.62328545, 0.64667545],\n",
    " [-1.74314104, -0.59664964]])\n",
    "    W = np.array([[ 0.74505627, 1.97611078, -1.24412333]])\n",
    "    b = np.array([[1]])\n",
    "    \"\"\"\n",
    "    A = np.random.randn(3,2)\n",
    "    W = np.random.randn(1,3)\n",
    "    b = np.random.randn(1,1)\n",
    "    \n",
    "    return A, W, b\n",
    "\n",
    "\n",
    "A,W,b = linear_forward_test_case()\n",
    "Z,linear_cache = linear_forward(A,W,b)\n",
    "print(\"Z = \" + str(Z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_activation_forward(A_prev,W,b,activation):\n",
    "    \"\"\"\n",
    "    实现LINEAR-> ACTIVATION 这一层的前向传播\n",
    "\n",
    "    参数：\n",
    "        A_prev - 来自上一层（或输入层）的激活，维度为(上一层的节点数量，示例数）\n",
    "        W - 权重矩阵，numpy数组，维度为（当前层的节点数量，前一层的大小）\n",
    "        b - 偏向量，numpy阵列，维度为（当前层的节点数量，1）\n",
    "        activation - 选择在此层中使用的激活函数名，字符串类型，【\"sigmoid\" | \"relu\"】\n",
    "\n",
    "    返回：\n",
    "        A - 激活函数的输出，也称为激活后的值\n",
    "        cache - 一个包含“linear_cache”和“activation_cache”的字典，我们需要存储它以有效地计算后向传递\n",
    "    \"\"\"\n",
    "    \n",
    "    if activation == \"sigmoid\":\n",
    "        Z, linear_cache = linear_forward(A_prev, W, b)\n",
    "        A, activation_cache = sigmoid(Z)\n",
    "    elif activation == \"relu\":\n",
    "        Z, linear_cache = linear_forward(A_prev, W, b)\n",
    "        A, activation_cache = relu(Z)\n",
    "    \n",
    "    assert(A.shape == (W.shape[0],A_prev.shape[1]))\n",
    "    cache = (linear_cache,activation_cache)\n",
    "    \n",
    "    return A,cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试linear_activation_forward==============\n",
      "sigmoid，A = [[0.96890023 0.11013289]]\n",
      "ReLU，A = [[3.43896131 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "#测试linear_activation_forward\n",
    "print(\"==============测试linear_activation_forward==============\")\n",
    "\n",
    "def linear_activation_forward_test_case():\n",
    "    \"\"\"\n",
    "    X = np.array([[-1.02387576, 1.12397796],\n",
    " [-1.62328545, 0.64667545],\n",
    " [-1.74314104, -0.59664964]])\n",
    "    W = np.array([[ 0.74505627, 1.97611078, -1.24412333]])\n",
    "    b = 5\n",
    "    \"\"\"\n",
    "    np.random.seed(2)\n",
    "    A_prev = np.random.randn(3,2)\n",
    "    W = np.random.randn(1,3)\n",
    "    b = np.random.randn(1,1)\n",
    "    return A_prev, W, b\n",
    "\n",
    "\n",
    "A_prev, W,b = linear_activation_forward_test_case()\n",
    "\n",
    "A, linear_activation_cache = linear_activation_forward(A_prev, W, b, activation = \"sigmoid\")\n",
    "print(\"sigmoid，A = \" + str(A))\n",
    "\n",
    "A, linear_activation_cache = linear_activation_forward(A_prev, W, b, activation = \"relu\")\n",
    "print(\"ReLU，A = \" + str(A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L_model_forward(X,parameters):\n",
    "    \"\"\"\n",
    "    实现[LINEAR-> RELU] *（L-1） - > LINEAR-> SIGMOID计算前向传播，也就是多层网络的前向传播，为后面每一层都执行LINEAR和ACTIVATION\n",
    "    \n",
    "    参数：\n",
    "        X - 数据，numpy数组，维度为（输入节点数量，示例数）\n",
    "        parameters - initialize_parameters_deep（）的输出\n",
    "    \n",
    "    返回：\n",
    "        AL - 最后的激活值\n",
    "        caches - 包含以下内容的缓存列表：\n",
    "                 linear_relu_forward（）的每个cache（有L-1个，索引为从0到L-2）\n",
    "                 linear_sigmoid_forward（）的cache（只有一个，索引为L-1）\n",
    "    \"\"\"\n",
    "    caches = []\n",
    "    A = X\n",
    "    L = len(parameters) // 2\n",
    "    for l in range(1,L):\n",
    "        A_prev = A \n",
    "        A, cache = linear_activation_forward(A_prev, parameters['W' + str(l)], parameters['b' + str(l)], \"relu\")\n",
    "        caches.append(cache)\n",
    "    \n",
    "    AL, cache = linear_activation_forward(A, parameters['W' + str(L)], parameters['b' + str(L)], \"sigmoid\")\n",
    "    caches.append(cache)\n",
    "    \n",
    "    assert(AL.shape == (1,X.shape[1]))\n",
    "    \n",
    "    return AL,caches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试L_model_forward==============\n",
      "AL = [[0.17007265 0.2524272 ]]\n",
      "caches 的长度为 = 2\n"
     ]
    }
   ],
   "source": [
    "#测试L_model_forward\n",
    "print(\"==============测试L_model_forward==============\")\n",
    "\n",
    "def L_model_forward_test_case():\n",
    "    \"\"\"\n",
    "    X = np.array([[-1.02387576, 1.12397796],\n",
    " [-1.62328545, 0.64667545],\n",
    " [-1.74314104, -0.59664964]])\n",
    "    parameters = {'W1': np.array([[ 1.62434536, -0.61175641, -0.52817175],\n",
    "        [-1.07296862,  0.86540763, -2.3015387 ]]),\n",
    " 'W2': np.array([[ 1.74481176, -0.7612069 ]]),\n",
    " 'b1': np.array([[ 0.],\n",
    "        [ 0.]]),\n",
    " 'b2': np.array([[ 0.]])}\n",
    "    \"\"\"\n",
    "    np.random.seed(1)\n",
    "    X = np.random.randn(4,2)\n",
    "    W1 = np.random.randn(3,4)\n",
    "    b1 = np.random.randn(3,1)\n",
    "    W2 = np.random.randn(1,3)\n",
    "    b2 = np.random.randn(1,1)\n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return X, parameters\n",
    "\n",
    "X,parameters = L_model_forward_test_case()\n",
    "AL,caches = L_model_forward(X,parameters)\n",
    "print(\"AL = \" + str(AL))\n",
    "print(\"caches 的长度为 = \" + str(len(caches)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cost(AL,Y):\n",
    "    \"\"\"\n",
    "    实施等式（4）定义的成本函数。\n",
    "\n",
    "    参数：\n",
    "        AL - 与标签预测相对应的概率向量，维度为（1，示例数量）\n",
    "        Y - 标签向量（例如：如果不是猫，则为0，如果是猫则为1），维度为（1，数量）\n",
    "\n",
    "    返回：\n",
    "        cost - 交叉熵成本\n",
    "    \"\"\"\n",
    "    m = Y.shape[1]\n",
    "    cost = -np.sum(np.multiply(np.log(AL),Y) + np.multiply(np.log(1 - AL), 1 - Y)) / m\n",
    "        \n",
    "    cost = np.squeeze(cost)\n",
    "    assert(cost.shape == ())\n",
    "\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试compute_cost==============\n",
      "cost = 0.414931599615397\n"
     ]
    }
   ],
   "source": [
    "#测试compute_cost\n",
    "print(\"==============测试compute_cost==============\")\n",
    "\n",
    "def compute_cost_test_case():\n",
    "    Y = np.asarray([[1, 1, 1]])\n",
    "    aL = np.array([[.8,.9,0.4]])\n",
    "    \n",
    "    return Y, aL\n",
    "\n",
    "Y,AL = compute_cost_test_case()\n",
    "print(\"cost = \" + str(compute_cost(AL, Y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_backward(dZ,cache):\n",
    "    \"\"\"\n",
    "    为单层实现反向传播的线性部分（第L层）\n",
    "\n",
    "    参数：\n",
    "         dZ - 相对于（当前第l层的）线性输出的成本梯度\n",
    "         cache - 来自当前层前向传播的值的元组（A_prev，W，b）\n",
    "\n",
    "    返回：\n",
    "         dA_prev - 相对于激活（前一层l-1）的成本梯度，与A_prev维度相同\n",
    "         dW - 相对于W（当前层l）的成本梯度，与W的维度相同\n",
    "         db - 相对于b（当前层l）的成本梯度，与b维度相同\n",
    "    \"\"\"\n",
    "    A_prev, W, b = cache\n",
    "    m = A_prev.shape[1]\n",
    "    dW = np.dot(dZ, A_prev.T) / m\n",
    "    db = np.sum(dZ, axis=1, keepdims=True) / m\n",
    "    dA_prev = np.dot(W.T, dZ)\n",
    "    \n",
    "    assert (dA_prev.shape == A_prev.shape)\n",
    "    assert (dW.shape == W.shape)\n",
    "    assert (db.shape == b.shape)\n",
    "    \n",
    "    return dA_prev, dW, db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试linear_backward==============\n",
      "dA_prev = [[ 0.51822968 -0.19517421]\n",
      " [-0.40506361  0.15255393]\n",
      " [ 2.37496825 -0.89445391]]\n",
      "dW = [[-0.10076895  1.40685096  1.64992505]]\n",
      "db = [[0.50629448]]\n"
     ]
    }
   ],
   "source": [
    "#测试linear_backward\n",
    "print(\"==============测试linear_backward==============\")\n",
    "\n",
    "def linear_backward_test_case():\n",
    "    \"\"\"\n",
    "    z, linear_cache = (np.array([[-0.8019545 ,  3.85763489]]), (np.array([[-1.02387576,  1.12397796],\n",
    "       [-1.62328545,  0.64667545],\n",
    "       [-1.74314104, -0.59664964]]), np.array([[ 0.74505627,  1.97611078, -1.24412333]]), np.array([[1]]))\n",
    "    \"\"\"\n",
    "    np.random.seed(1)\n",
    "    dZ = np.random.randn(1,2)\n",
    "    A = np.random.randn(3,2)\n",
    "    W = np.random.randn(1,3)\n",
    "    b = np.random.randn(1,1)\n",
    "    linear_cache = (A, W, b)\n",
    "    return dZ, linear_cache\n",
    "\n",
    "dZ, linear_cache = linear_backward_test_case()\n",
    "\n",
    "dA_prev, dW, db = linear_backward(dZ, linear_cache)\n",
    "print (\"dA_prev = \"+ str(dA_prev))\n",
    "print (\"dW = \" + str(dW))\n",
    "print (\"db = \" + str(db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_activation_backward(dA,cache,activation=\"relu\"):\n",
    "    \"\"\"\n",
    "    实现LINEAR-> ACTIVATION层的后向传播。\n",
    "    \n",
    "    参数：\n",
    "         dA - 当前层l的激活后的梯度值\n",
    "         cache - 我们存储的用于有效计算反向传播的值的元组（值为linear_cache，activation_cache）\n",
    "         activation - 要在此层中使用的激活函数名，字符串类型，【\"sigmoid\" | \"relu\"】\n",
    "    返回：\n",
    "         dA_prev - 相对于激活（前一层l-1）的成本梯度值，与A_prev维度相同\n",
    "         dW - 相对于W（当前层l）的成本梯度值，与W的维度相同\n",
    "         db - 相对于b（当前层l）的成本梯度值，与b的维度相同\n",
    "    \"\"\"\n",
    "    linear_cache, activation_cache = cache\n",
    "    if activation == \"relu\":\n",
    "        dZ = relu_backward(dA, activation_cache)\n",
    "        dA_prev, dW, db = linear_backward(dZ, linear_cache)\n",
    "    elif activation == \"sigmoid\":\n",
    "        dZ = sigmoid_backward(dA, activation_cache)\n",
    "        dA_prev, dW, db = linear_backward(dZ, linear_cache)\n",
    "    \n",
    "    return dA_prev,dW,db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试linear_activation_backward==============\n",
      "sigmoid:\n",
      "dA_prev = [[ 0.11017994  0.01105339]\n",
      " [ 0.09466817  0.00949723]\n",
      " [-0.05743092 -0.00576154]]\n",
      "dW = [[ 0.10266786  0.09778551 -0.01968084]]\n",
      "db = [[-0.05729622]]\n",
      "\n",
      "relu:\n",
      "dA_prev = [[ 0.44090989 -0.        ]\n",
      " [ 0.37883606 -0.        ]\n",
      " [-0.2298228   0.        ]]\n",
      "dW = [[ 0.44513824  0.37371418 -0.10478989]]\n",
      "db = [[-0.20837892]]\n"
     ]
    }
   ],
   "source": [
    "#测试linear_activation_backward\n",
    "print(\"==============测试linear_activation_backward==============\")\n",
    "\n",
    "def linear_activation_backward_test_case():\n",
    "    \"\"\"\n",
    "    aL, linear_activation_cache = (np.array([[ 3.1980455 ,  7.85763489]]), ((np.array([[-1.02387576,  1.12397796], [-1.62328545,  0.64667545], [-1.74314104, -0.59664964]]), np.array([[ 0.74505627,  1.97611078, -1.24412333]]), 5), np.array([[ 3.1980455 ,  7.85763489]])))\n",
    "    \"\"\"\n",
    "    np.random.seed(2)\n",
    "    dA = np.random.randn(1,2)\n",
    "    A = np.random.randn(3,2)\n",
    "    W = np.random.randn(1,3)\n",
    "    b = np.random.randn(1,1)\n",
    "    Z = np.random.randn(1,2)\n",
    "    linear_cache = (A, W, b)\n",
    "    activation_cache = Z\n",
    "    linear_activation_cache = (linear_cache, activation_cache)\n",
    "    \n",
    "    return dA, linear_activation_cache\n",
    "\n",
    "\n",
    "\n",
    "AL, linear_activation_cache = linear_activation_backward_test_case()\n",
    " \n",
    "dA_prev, dW, db = linear_activation_backward(AL, linear_activation_cache, activation = \"sigmoid\")\n",
    "print (\"sigmoid:\")\n",
    "print (\"dA_prev = \"+ str(dA_prev))\n",
    "print (\"dW = \" + str(dW))\n",
    "print (\"db = \" + str(db) + \"\\n\")\n",
    " \n",
    "dA_prev, dW, db = linear_activation_backward(AL, linear_activation_cache, activation = \"relu\")\n",
    "print (\"relu:\")\n",
    "print (\"dA_prev = \"+ str(dA_prev))\n",
    "print (\"dW = \" + str(dW))\n",
    "print (\"db = \" + str(db))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L_model_backward(AL,Y,caches):\n",
    "    \"\"\"\n",
    "    对[LINEAR-> RELU] *（L-1） - > LINEAR - > SIGMOID组执行反向传播，就是多层网络的向后传播\n",
    "    \n",
    "    参数：\n",
    "     AL - 概率向量，正向传播的输出（L_model_forward（））\n",
    "     Y - 标签向量（例如：如果不是猫，则为0，如果是猫则为1），维度为（1，数量）\n",
    "     caches - 包含以下内容的cache列表：\n",
    "                 linear_activation_forward（\"relu\"）的cache，不包含输出层\n",
    "                 linear_activation_forward（\"sigmoid\"）的cache\n",
    "    \n",
    "    返回：\n",
    "     grads - 具有梯度值的字典\n",
    "              grads [“dA”+ str（l）] = ...\n",
    "              grads [“dW”+ str（l）] = ...\n",
    "              grads [“db”+ str（l）] = ...\n",
    "    \"\"\"\n",
    "    grads = {}\n",
    "    L = len(caches)\n",
    "    m = AL.shape[1]\n",
    "    Y = Y.reshape(AL.shape)\n",
    "    dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))\n",
    "    \n",
    "    current_cache = caches[L-1]\n",
    "    grads[\"dA\" + str(L)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = linear_activation_backward(dAL, current_cache, \"sigmoid\")\n",
    "    \n",
    "    for l in reversed(range(L-1)):\n",
    "        current_cache = caches[l]\n",
    "        dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads[\"dA\" + str(l + 2)], current_cache, \"relu\")\n",
    "        grads[\"dA\" + str(l + 1)] = dA_prev_temp\n",
    "        grads[\"dW\" + str(l + 1)] = dW_temp\n",
    "        grads[\"db\" + str(l + 1)] = db_temp\n",
    "    \n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试L_model_backward==============\n",
      "dW1 = [[0.41010002 0.07807203 0.13798444 0.10502167]\n",
      " [0.         0.         0.         0.        ]\n",
      " [0.05283652 0.01005865 0.01777766 0.0135308 ]]\n",
      "db1 = [[-0.22007063]\n",
      " [ 0.        ]\n",
      " [-0.02835349]]\n",
      "dA1 = [[ 0.          0.52257901]\n",
      " [ 0.         -0.3269206 ]\n",
      " [ 0.         -0.32070404]\n",
      " [ 0.         -0.74079187]]\n"
     ]
    }
   ],
   "source": [
    "#测试L_model_backward\n",
    "print(\"==============测试L_model_backward==============\")\n",
    "\n",
    "def L_model_backward_test_case():\n",
    "    \"\"\"\n",
    "    X = np.random.rand(3,2)\n",
    "    Y = np.array([[1, 1]])\n",
    "    parameters = {'W1': np.array([[ 1.78862847,  0.43650985,  0.09649747]]), 'b1': np.array([[ 0.]])}\n",
    "\n",
    "    aL, caches = (np.array([[ 0.60298372,  0.87182628]]), [((np.array([[ 0.20445225,  0.87811744],\n",
    "           [ 0.02738759,  0.67046751],\n",
    "           [ 0.4173048 ,  0.55868983]]),\n",
    "    np.array([[ 1.78862847,  0.43650985,  0.09649747]]),\n",
    "    np.array([[ 0.]])),\n",
    "   np.array([[ 0.41791293,  1.91720367]]))])\n",
    "   \"\"\"\n",
    "    np.random.seed(3)\n",
    "    AL = np.random.randn(1, 2)\n",
    "    Y = np.array([[1, 0]])\n",
    "\n",
    "    A1 = np.random.randn(4,2)\n",
    "    W1 = np.random.randn(3,4)\n",
    "    b1 = np.random.randn(3,1)\n",
    "    Z1 = np.random.randn(3,2)\n",
    "    linear_cache_activation_1 = ((A1, W1, b1), Z1)\n",
    "\n",
    "    A2 = np.random.randn(3,2)\n",
    "    W2 = np.random.randn(1,3)\n",
    "    b2 = np.random.randn(1,1)\n",
    "    Z2 = np.random.randn(1,2)\n",
    "    linear_cache_activation_2 = ( (A2, W2, b2), Z2)\n",
    "\n",
    "    caches = (linear_cache_activation_1, linear_cache_activation_2)\n",
    "\n",
    "    return AL, Y, caches\n",
    "\n",
    "\n",
    "AL, Y_assess, caches = L_model_backward_test_case()\n",
    "grads = L_model_backward(AL, Y_assess, caches)\n",
    "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n",
    "print (\"db1 = \"+ str(grads[\"db1\"]))\n",
    "print (\"dA1 = \"+ str(grads[\"dA1\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters(parameters, grads, learning_rate):\n",
    "    \"\"\"\n",
    "    使用梯度下降更新参数\n",
    "    \n",
    "    参数：\n",
    "     parameters - 包含你的参数的字典\n",
    "     grads - 包含梯度值的字典，是L_model_backward的输出\n",
    "    \n",
    "    返回：\n",
    "     parameters - 包含更新参数的字典\n",
    "                   参数[“W”+ str（l）] = ...\n",
    "                   参数[“b”+ str（l）] = ...\n",
    "    \"\"\"\n",
    "    L = len(parameters) // 2 #整除\n",
    "    for l in range(L):\n",
    "        parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * grads[\"dW\" + str(l + 1)]\n",
    "        parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * grads[\"db\" + str(l + 1)]\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============测试update_parameters==============\n",
      "W1 = [[-0.59562069 -0.09991781 -2.14584584  1.82662008]\n",
      " [-1.76569676 -0.80627147  0.51115557 -1.18258802]\n",
      " [-1.0535704  -0.86128581  0.68284052  2.20374577]]\n",
      "b1 = [[-0.04659241]\n",
      " [-1.28888275]\n",
      " [ 0.53405496]]\n",
      "W2 = [[-0.55569196  0.0354055   1.32964895]]\n",
      "b2 = [[-0.84610769]]\n"
     ]
    }
   ],
   "source": [
    "#测试update_parameters\n",
    "print(\"==============测试update_parameters==============\")\n",
    "\n",
    "def update_parameters_test_case():\n",
    "    \"\"\"\n",
    "    parameters = {'W1': np.array([[ 1.78862847,  0.43650985,  0.09649747],\n",
    "        [-1.8634927 , -0.2773882 , -0.35475898],\n",
    "        [-0.08274148, -0.62700068, -0.04381817],\n",
    "        [-0.47721803, -1.31386475,  0.88462238]]),\n",
    " 'W2': np.array([[ 0.88131804,  1.70957306,  0.05003364, -0.40467741],\n",
    "        [-0.54535995, -1.54647732,  0.98236743, -1.10106763],\n",
    "        [-1.18504653, -0.2056499 ,  1.48614836,  0.23671627]]),\n",
    " 'W3': np.array([[-1.02378514, -0.7129932 ,  0.62524497],\n",
    "        [-0.16051336, -0.76883635, -0.23003072]]),\n",
    " 'b1': np.array([[ 0.],\n",
    "        [ 0.],\n",
    "        [ 0.],\n",
    "        [ 0.]]),\n",
    " 'b2': np.array([[ 0.],\n",
    "        [ 0.],\n",
    "        [ 0.]]),\n",
    " 'b3': np.array([[ 0.],\n",
    "        [ 0.]])}\n",
    "    grads = {'dW1': np.array([[ 0.63070583,  0.66482653,  0.18308507],\n",
    "        [ 0.        ,  0.        ,  0.        ],\n",
    "        [ 0.        ,  0.        ,  0.        ],\n",
    "        [ 0.        ,  0.        ,  0.        ]]),\n",
    " 'dW2': np.array([[ 1.62934255,  0.        ,  0.        ,  0.        ],\n",
    "        [ 0.        ,  0.        ,  0.        ,  0.        ],\n",
    "        [ 0.        ,  0.        ,  0.        ,  0.        ]]),\n",
    " 'dW3': np.array([[-1.40260776,  0.        ,  0.        ]]),\n",
    " 'da1': np.array([[ 0.70760786,  0.65063504],\n",
    "        [ 0.17268975,  0.15878569],\n",
    "        [ 0.03817582,  0.03510211]]),\n",
    " 'da2': np.array([[ 0.39561478,  0.36376198],\n",
    "        [ 0.7674101 ,  0.70562233],\n",
    "        [ 0.0224596 ,  0.02065127],\n",
    "        [-0.18165561, -0.16702967]]),\n",
    " 'da3': np.array([[ 0.44888991,  0.41274769],\n",
    "        [ 0.31261975,  0.28744927],\n",
    "        [-0.27414557, -0.25207283]]),\n",
    " 'db1': 0.75937676204411464,\n",
    " 'db2': 0.86163759922811056,\n",
    " 'db3': -0.84161956022334572}\n",
    "    \"\"\"\n",
    "    np.random.seed(2)\n",
    "    W1 = np.random.randn(3,4)\n",
    "    b1 = np.random.randn(3,1)\n",
    "    W2 = np.random.randn(1,3)\n",
    "    b2 = np.random.randn(1,1)\n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    np.random.seed(3)\n",
    "    dW1 = np.random.randn(3,4)\n",
    "    db1 = np.random.randn(3,1)\n",
    "    dW2 = np.random.randn(1,3)\n",
    "    db2 = np.random.randn(1,1)\n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2}\n",
    "    \n",
    "    return parameters, grads\n",
    "\n",
    "parameters, grads = update_parameters_test_case()\n",
    "parameters = update_parameters(parameters, grads, 0.1)\n",
    " \n",
    "print (\"W1 = \"+ str(parameters[\"W1\"]))\n",
    "print (\"b1 = \"+ str(parameters[\"b1\"]))\n",
    "print (\"W2 = \"+ str(parameters[\"W2\"]))\n",
    "print (\"b2 = \"+ str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def two_layer_model(X,Y,layers_dims,learning_rate=0.0075,num_iterations=3000,print_cost=False,isPlot=True):\n",
    "    \"\"\"\n",
    "    实现一个两层的神经网络，【LINEAR->RELU】 -> 【LINEAR->SIGMOID】\n",
    "    参数：\n",
    "        X - 输入的数据，维度为(n_x，例子数)\n",
    "        Y - 标签，向量，0为非猫，1为猫，维度为(1,数量)\n",
    "        layers_dims - 层数的向量，维度为(n_y,n_h,n_y)\n",
    "        learning_rate - 学习率\n",
    "        num_iterations - 迭代的次数\n",
    "        print_cost - 是否打印成本值，每100次打印一次\n",
    "        isPlot - 是否绘制出误差值的图谱\n",
    "    返回:\n",
    "        parameters - 一个包含W1，b1，W2，b2的字典变量\n",
    "    \"\"\"\n",
    "    np.random.seed(1)\n",
    "    grads = {}\n",
    "    costs = []\n",
    "    (n_x,n_h,n_y) = layers_dims\n",
    "    \n",
    "    \"\"\"\n",
    "    初始化参数\n",
    "    \"\"\"\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    \n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    \n",
    "    \"\"\"\n",
    "    开始进行迭代\n",
    "    \"\"\"\n",
    "    for i in range(0,num_iterations):\n",
    "        #前向传播\n",
    "        A1, cache1 = linear_activation_forward(X, W1, b1, \"relu\")\n",
    "        A2, cache2 = linear_activation_forward(A1, W2, b2, \"sigmoid\")\n",
    "        \n",
    "        #计算成本\n",
    "        cost = compute_cost(A2,Y)\n",
    "        \n",
    "        #后向传播\n",
    "        ##初始化后向传播\n",
    "        dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))\n",
    "        \n",
    "        ##向后传播，输入：“dA2，cache2，cache1”。 输出：“dA1，dW2，db2;还有dA0（未使用），dW1，db1”。\n",
    "        dA1, dW2, db2 = linear_activation_backward(dA2, cache2, \"sigmoid\")\n",
    "        dA0, dW1, db1 = linear_activation_backward(dA1, cache1, \"relu\")\n",
    "        \n",
    "        ##向后传播完成后的数据保存到grads\n",
    "        grads[\"dW1\"] = dW1\n",
    "        grads[\"db1\"] = db1\n",
    "        grads[\"dW2\"] = dW2\n",
    "        grads[\"db2\"] = db2\n",
    "        \n",
    "        #更新参数\n",
    "        parameters = update_parameters(parameters,grads,learning_rate)\n",
    "        W1 = parameters[\"W1\"]\n",
    "        b1 = parameters[\"b1\"]\n",
    "        W2 = parameters[\"W2\"]\n",
    "        b2 = parameters[\"b2\"]\n",
    "        \n",
    "        #打印成本值，如果print_cost=False则忽略\n",
    "        if i % 100 == 0:\n",
    "            #记录成本\n",
    "            costs.append(cost)\n",
    "            #是否打印成本值\n",
    "            if print_cost:\n",
    "                print(\"第\", i ,\"次迭代，成本值为：\" ,np.squeeze(cost))\n",
    "    #迭代完成，根据条件绘制图\n",
    "    if isPlot:\n",
    "        plt.plot(np.squeeze(costs))\n",
    "        plt.ylabel('cost')\n",
    "        plt.xlabel('iterations (per tens)')\n",
    "        plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "        plt.show()\n",
    "    \n",
    "    #返回parameters\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataset():\n",
    "    train_dataset = h5py.File('/home/xieyipeng/Documents/MachineLearning/wuenda_py/couse1/do_not_src/week_4_2/datasets/train_catvnoncat.h5', \"r\")\n",
    "    train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n",
    "    train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n",
    "\n",
    "    test_dataset = h5py.File('/home/xieyipeng/Documents/MachineLearning/wuenda_py/couse1/do_not_src/week_4_2/datasets/test_catvnoncat.h5', \"r\")\n",
    "    test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n",
    "    test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n",
    "\n",
    "    classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n",
    "    \n",
    "    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n",
    "    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n",
    "    \n",
    "    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\n",
    "\n",
    "\n",
    "train_set_x_orig , train_set_y , test_set_x_orig , test_set_y , classes = load_dataset()\n",
    "\n",
    "train_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T \n",
    "test_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T\n",
    "\n",
    "train_x = train_x_flatten / 255\n",
    "train_y = train_set_y\n",
    "test_x = test_x_flatten / 255\n",
    "test_y = test_set_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0 次迭代，成本值为： 0.6930497356599891\n",
      "第 100 次迭代，成本值为： 0.6464320953428849\n",
      "第 200 次迭代，成本值为： 0.6325140647912677\n",
      "第 300 次迭代，成本值为： 0.6015024920354665\n",
      "第 400 次迭代，成本值为： 0.5601966311605748\n",
      "第 500 次迭代，成本值为： 0.515830477276473\n",
      "第 600 次迭代，成本值为： 0.4754901313943326\n",
      "第 700 次迭代，成本值为： 0.4339163151225749\n",
      "第 800 次迭代，成本值为： 0.4007977536203887\n",
      "第 900 次迭代，成本值为： 0.3580705011323798\n",
      "第 1000 次迭代，成本值为： 0.3394281538366412\n",
      "第 1100 次迭代，成本值为： 0.3052753636196264\n",
      "第 1200 次迭代，成本值为： 0.27491377282130164\n",
      "第 1300 次迭代，成本值为： 0.2468176821061485\n",
      "第 1400 次迭代，成本值为： 0.19850735037466086\n",
      "第 1500 次迭代，成本值为： 0.17448318112556638\n",
      "第 1600 次迭代，成本值为： 0.17080762978096067\n",
      "第 1700 次迭代，成本值为： 0.11306524562164735\n",
      "第 1800 次迭代，成本值为： 0.09629426845937158\n",
      "第 1900 次迭代，成本值为： 0.08342617959726861\n",
      "第 2000 次迭代，成本值为： 0.0743907870431908\n",
      "第 2100 次迭代，成本值为： 0.06630748132267929\n",
      "第 2200 次迭代，成本值为： 0.059193295010381695\n",
      "第 2300 次迭代，成本值为： 0.05336140348560557\n",
      "第 2400 次迭代，成本值为： 0.048554785628770164\n"
     ]
    },
    {
     "data": {
      "image/png": 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zu/POl6sZ+sRk7XwWkW8Jc0uhHzDf3Re6+05gHDAwxPeTSrjkqHbcc24PPpxXzJAxk9i6U4erisjXwgyFVsDSuOdFwbTyzjWz6Wb2vJm1SbQgM7vGzArNrLC4WCOBHqwL+rbh3vN7MmHhWq4YPUnnMYhImTBDIdHFg73c81eA9u7eA3gbGJtoQe4+wt0L3L2gadOmVVxmcvrxka15YFBvJi9ez+WjPmPj9l1RlyQiNUCYoVAExP/ybw0sj2/g7mvdfUfwdCTQJ8R6pJyzerbk4cG9mbZ0A5c+/hklWxUMIskuzFCYBHQ2sw5mlgEMAsbHNzCz+EuFnQXMDrEeSeC0I1rw10v6MHv5Ri56bILGShJJcqGFgruXAsOAN4h92T/n7jPN7E4zOytodpOZzTSzacBNwBVh1SMVO7lrMx69rA/zVm9m8MgJrNm8Y/8vEpE6ydzLd/PXbAUFBV5YWBh1GXXSR/PWcPUTk2jTqB5PDe3PIblZUZckIlXEzCa7e8H+2um0VikzoHMTxlzZj2UbtjHo0Qm6HoNIElIoyDcc1TGfJ4b0Y/WmHVw44lOWb9gWdUkiUo0UCvItBe0b8/er+rFu804Gj5zAihIFg0iyUChIQr3bNuKJvcEwQl1JIslCoSAV6t22EWOv6seaYItBwSBS9ykUZJ+ObNuIsUP6UbxpB4NHTmDVRgWDSF2mUJD96tOuEWOH9GX1xu0MHqFgEKnLFApSKX3aNWbskH6s2ridwSMnsFrBIFInKRSk0graN2bMkH6sLAmCYZOCQaSuUSjIAenbPrbFsKIk1pVUvElDYojUJQoFOWB92zdmzJVBMIxUMIjUJQoF+U76dWjM6Cv6smz9Ni5SMIjUGQoF+c76d8xn9JV9KVq/jYsf0+iqInWBQkEOylEd8xl1RV+WrNvKxSMnslbBIFKrKRTkoB3dKRYMi9dt4fxHP2VB8eaoSxKR70ihIFXimE5NeGJIfzZs3cXZD3/MO7NXRV2SiHwHCgWpMv06NGb8sGNp16QeVz9RyEPvzGPPntp1ESeRZKdQkCrVulE9nr/2GM7u1Yr73prLtU9OZvOO0qjLEpFKUihIlctKT+W+C3ry2zO68s6Xqzl7+Mcs1H4GkVpBoSChMDOGDOgQu1jPlp0MHP4x//lS+xlEajqFgoTqmE5NGD/sWNo2rsdVYwv5i/YziNRoCgUJ3d79DAN7tuTet+Zy3VPazyBSU4UaCmZ2qpnNMbP5ZnbbPtqdZ2ZuZgVh1iPRyc5I5f4Le/GbH32Pt2ev5pzhH/PVmi1RlyUi5YQWCmaWCgwHTgO6AoPNrGuCdrnATcDEsGqRmsHMuPq4jvx9SD/WbN7BWQ9/xLtfro66LBGJE+aWQj9gvrsvdPedwDhgYIJ2dwH3ABqcP0kcc2gTxg8bQJtG9RgydhJPfLoo6pJEJBBmKLQClsY9LwqmlTGz3kAbd391Xwsys2vMrNDMCouLi6u+Uql2bRrX44XrjuGk7zXjt/+cyTOfLYm6JBGhkqFgZudXZlr5JgmmlR12YmYpwP3Az/b3/u4+wt0L3L2gadOm+2sutUR2RioPX9SbHxzWlF+9NIMXJhdFXZJI0qvslsIvKzktXhHQJu55a2B53PNcoDvwnpktAo4Cxmtnc3LJTEvlr5f04ZhO+fzP89N4Zdry/b9IREKTtq+ZZnYacDrQysweipvVANjfMYWTgM5m1gFYBgwCLto7091LgCZx7/Ue8N/uXnggKyC1X1Z6KiMvK+CKUZO45dmpZKSl8MNuzaMuSyQp7W9LYTlQSGwn8OS423jgh/t6obuXAsOAN4DZwHPuPtPM7jSzsw62cKlb6mWkMerKvvRoncewpz/XUUkiETH3/Z9dambp7r4reNyI2M7h6WEXl0hBQYEXFmpjoq4q2baLix+bwNxVmxl9RV+OPbTJ/l8kIvtlZpPdfb/d85Xdp/CWmTUws8bANGC0md13UBWKJJCXnc7fh/SnY5Mcrho7iYkL10ZdkkhSqWwo5Ln7RuDHwGh37wOcFF5Zkswa5WTw96v606phNkPGTOLzJeujLkkkaVQ2FNLMrAVwAbDPcwpEqkLT3EyeHnoUTXIzuXzUZ3yxrCTqkkSSQmVD4U5iO4wXuPskM+sIzAuvLBFo1iCLp4ceRYOsdC55fCJfrtwYdUkidV6lQsHd/+HuPdz9uuD5Qnc/N9zSRKBVw2yeHtqfrLRULh45kfmrN0VdkkidVtkzmlub2UtmttrMVpnZC2bWOuziRADa5efw1ND+mBkXjZzIIo2uKhKaynYfjSZ2bkJLYuMXvRJME6kWnZrW5+mh/Snd4wweOYGXpyxj1+49UZclUudUNhSauvtody8NbmMADUIk1apLs1z+flU/cjLTuOXZqRx/z7s8+v4CNm7fFXVpInVGZUNhjZldYmapwe0SQAeQS7Xr1jKPN285ntFX9KV9fg5/+NeXHPOH/3DXq7MoWr816vJEar3KntHcFngYOJrYSKefADe5e7WPd6wzmiXeF8tKeOzDhbwyfQUApx/RgqHHdaBH64YRVyZSs1T2jObKhsJY4BZ3Xx88bwz82d2HHHSlB0ihIIks37CNMZ8s4pmJS9i0o5T+HRoz9LiOnHj4IaSkJBrFXSS5VHUoTHH33vubVh0UCrIvm7bv4tlJSxn10VcsL9lOx6Y5DD2uI+f0bkVWemrU5YlEpqrHPkoJBsLbu/DG7GfYbZEo5Galc/VxHXn/5z/gwUG9qJeRyi9fnMHJ97+vk99EKqGyoXAv8ImZ3WVmdxLbp3BPeGWJHJz01BQG9mrFK8MG8ORV/dmxaw/nPvIJb85cGXVpIjVaZc9ofgI4F1gFFAM/dve/h1mYSFUwMwZ0bsIrNw7g0EPq85MnJzP83flUpttUJBlVugvI3WcBs0KsRSQ0zRpk8exPjubnz0/nT2/MYe6qTfzfuT20n0GkHO0XkKSRlZ7Kg4N6cVjzXP70xhwWrdnCiMsKaNYgK+rSRGqMyu5TEKkTzIwbfnAoIy7tw7zVmznr4Y+YXrQh6rJEagyFgiSlU7o154XrjiEtJYXz//Yp46ctj7okkRpBoSBJ63stGjB+2LH0bN2Qm56Zwp/fmMOePdoBLclNoSBJLb9+Jk9e3Z9Bfdvw8LvzufbJyWzZURp1WSKRUShI0stIS+EPPz6C28/sytuzV3HuXz9h6ToNrifJKdRQMLNTzWyOmc03s9sSzL/WzGaY2VQz+8jMuoZZj0hFzIwrj+3AmCv7sWzDNgYO/5hPF2ggYEk+oYWCmaUCw4HTgK7A4ARf+k+7+xHu3ovYGdL3hVWPSGUc36Up/7zhWBrWS+fixyZw/1tz2a39DJJEwtxS6AfMD67nvBMYBwyMb+Du8YPR5BAbllskUh2b1ueVYQM4p3drHnxnHoNHTmBFybaoyxKpFmGGQitgadzzomDaN5jZDWa2gNiWwk2JFmRm15hZoZkVFhcXh1KsSLyczDTuvaAn913Qky+WlXDagx/y9qxVUZclErowQyHRIPbf2hJw9+Hu3gn4BfCbRAty9xHuXuDuBU2b6iqgUn1+fGRrXr1xAK0aZnP1E4X87pWZ7CjdHXVZIqEJMxSKgDZxz1sD+zpDaBxwdoj1iHwnHZvW58Xrj+HKY9sz+uNF/PiRT/hqzZaoyxIJRZihMAnobGYdzCwDGASMj29gZp3jnv4ImBdiPSLfWWZaKref2Y2RlxWwbMM2znjoQ16aUhR1WSJVLrRQcPdSYBjwBjAbeM7dZ5rZnWZ2VtBsmJnNNLOpwE+By8OqR6QqnNy1Gf+6+Ti6tczj1men8bPnpulkN6lTKnU5zppEl+OUmqB09x7+8p/5/OU/82ifn8NfLupNt5Z5UZclUqGqvhyniMRJS03h1pO78NTVR7FlZynnDP+EsZ8s0sV7pNZTKIgchKM75fOvm49nQOcm3D5+JpePnsTKku1RlyXynSkURA5S45wMHr+8gLsGdmPSV+s45f73eWlKkbYapFZSKIhUATPj0qPb86+bj+Ow5rnc+uw0rn1yMms274i6NJEDolAQqULtm+Qw7pqj+fXp3+PdOcWccv8H/PuLFVGXJVJpCgWRKpaaYgw9viOvBWdCX/vk59wybgolW3dFXZrIfikURELSuVkuL15/DLee1IVXp6/glAfe5905q6MuS2SfFAoiIUpPTeHmkzrz8g3HkpedzpWjJ3HbC9PZtF1bDVIzKRREqkH3Vnm8cuMArjuhE88VLuXUBz7kkwVroi5L5FsUCiLVJDMtlV+cejj/uPYYMtJSuGjkRP7w+mwduio1ikJBpJr1adeI1286jsH92vLoBwsZ/u78qEsSKZMWdQEiySg7I5Xfn9OdbTtL+fObc2mXn8OZPVtGXZaIthREomJm/N95PejbvhE/+8c0Ji9eF3VJIgoFkShlpqUy4tICWuZlMfSJySxZuzXqkiTJKRREItYoJ4NRV/RljztXjvlMJ7lJpBQKIjVAx6b1efSSPixZt5XrnprMztI9UZckSUqhIFJD9O+Yzx9/3INPFqzlNy/P0KGqEgkdfSRSg5zbpzWL127hof/Mp32THK4/4dCoS5Iko1AQqWFuPbkLi9Zu5Z5/z6Fd4xx+1KNF1CVJElH3kUgNY2bcc14PCto14qfPTeXzJeujLkmSiEJBpAbKSk/l0Uv70KxBFkPHFrJ0nQ5VleoRaiiY2almNsfM5pvZbQnm/9TMZpnZdDN7x8zahVmPSG2SXz+TUVf0ZdfuPVw5ZhIl23SoqoQvtFAws1RgOHAa0BUYbGZdyzWbAhS4ew/geeCesOoRqY0OPaQ+j15awOK1W7j+qcns2q1DVSVcYW4p9APmu/tCd98JjAMGxjdw93fdfe928QSgdYj1iNRKR3fK5w8/7sHH89fy/17+QoeqSqjCPPqoFbA07nkR0H8f7a8C/pVohpldA1wD0LZt26qqT6TWOK9Paxat2cLD784nNcX4+amHk5edHnVZUgeFGQqWYFrCnzhmdglQAHw/0Xx3HwGMACgoKNDPJElKPz25C1t37mb0J1/xry9W8t+nHMaFfduQmpLov5rIdxNm91ER0CbueWtgeflGZnYS8GvgLHffEWI9IrVaSorx2zO78sqwARzatD6/emkGZz38EZ99pdFVpeqEGQqTgM5m1sHMMoBBwPj4BmbWG3iUWCDoiuYildC9VR7P/uQo/jK4N+u37OSCRz/lxmemsHzDtqhLkzogtFBw91JgGPAGMBt4zt1nmtmdZnZW0OxPQH3gH2Y21czGV7A4EYljZpzZsyXv/OwEbvqvzrw5cyUn3vseD749j+27dkddntRiVtuOZCgoKPDCwsKoyxCpUYrWb+UPr3/JazNW0KphNr86/XucfkRzzLS/QWLMbLK7F+yvnc5oFqkDWjeqx/CLj2TcNUfRIDudG57+nMEjJzB7xcaoS5NaRlsKInXM7j3OM58t4d4351CybRcX9m3DkW0b0SQ3kyY5mTTJzSA/J5OMNP0mTCaV3VJQKIjUUSVbd3H/23N5csJiSvd8+/95XnY6+fUzaFI/k6b1M8seN6mfSc82eXRrmRdB1RIWhYKIALBt526KN+2gePMO1m7ewZrNO1mzeQdrNu9g7eadFAeP12zawcbtpQCkpRgvXn8MPVo3jLh6qSqVDQVdT0GkjsvOSKVtfj3a5tfbb9udpXtYvmEbF42cwM3jpvLqjQPIydTXRDJRp6KIlMlIS6F9kxzuu7AXi9Zu4XevzIy6JKlmCgUR+ZajOuZz/QmdeK6wiNemr4i6HKlGCgURSeiWk7rQs01DfvnidJbpbOmkoVAQkYTSU1N4aFAvdu9xbn12KrsTHMEkdY9CQUQq1C4/h98N7M5nX63jb+8viLocqQYKBRHZp3OPbMWZPVty31tzmbJkfdTlSMgUCiKyT2bG/57dneYNsrh53FQ27yiNuiQJkUJBRPYrLzudBwb1omj9Vm7/pw5TrcsUCiJSKX3bN2bYiZ154fMixk/71vWypI5QKIhIpd104qEc2bYhv35pBkXrt0ZdjoRAoSAilZaWmsKDg3rjDreMm0rp7j1RlyRVTKEgIgekTeN63HV2NwoXr2f4uzpMta5RKIjIATund0lDUygAAAzySURBVGvO7tWSh/4zj8mLdZhqXaJQEJHv5M6zu9MiL4ubx01h4/ZdUZcjVUShICLfSYOsdB4c1IsVJdv57ctfRF2OVBGFgoh8Z33aNeamEzvz8tTl3P/WXEq2aouhttPVM0TkoNzwg07MWFbCg+/M42/vL+DMni25uH9berVpiJlFXZ4coFC3FMzsVDObY2bzzey2BPOPN7PPzazUzM4LsxYRCUdaagqPXV7AqzcO4Nw+rfnXjBWc88gn/Oihj3hywmINi1HLhHaNZjNLBeYCJwNFwCRgsLvPimvTHmgA/Dcw3t2f399ydY1mkZpt845S/jl1GU9OWMLsFRvJyUhlYO9WXNy/Ld1a5kVdXtKqCddo7gfMd/eFQUHjgIFAWSi4+6Jgns6AEakj6memcXH/dlzUry1Tl27gqYlLeGFyEU9PXEKvNg25uH9bzujRkuyM1KhLlQTC7D5qBSyNe14UTDtgZnaNmRWaWWFxcXGVFCci4TIzerdtxJ/P78lnvzqJ357RlU3bd/E/z0+n/+/f5o7xM5m8eD17dPGeGiXMLYVEe5i+06fv7iOAERDrPjqYokSk+uXVS2fIgA5ceWx7PvtqHU9NXMJTExcz5pNFNKmfycldD+GUrs05ulM+WenagohSmKFQBLSJe94a0NCKIknMzOjfMZ/+HfMp2dad9+as5s1Zqxg/dTnPfLaUnIxUvn9YU07p2pwfHHYIefXSoy456YQZCpOAzmbWAVgGDAIuCvH9RKQWyctOZ2CvVgzs1Yodpbv5dMFa3py1irdmreL1GStJSzH6d2zMKV2bc3LXZrRsmB11yUkhtKOPAMzsdOABIBUY5e53m9mdQKG7jzezvsBLQCNgO7DS3bvta5k6+kikbtuzx5lWtIE3Z63izZkrWVC8BYDurRpw4uHNKGjXiJ5tGpKXra2IA1HZo49CDYUwKBREksuC4s28FQTElKUb2PuVdegh9endpiG92jakd5tGdGlWn7RUDdJQEYWCiNQ5m7bvYkZRCVOWbmDKkvV8vmQD67bsBKBeRio9WufRu20jerdpSO+2jWiamxlxxTVHTThPQUSkSuVmpXPMoU045tAmALg7S9dtY8rS9UxZEguKkR8spDQ4zLV1o2x6tm5It1YNOKJVHt1a5tE4JyPKVajxFAoiUmuZGW3z69E2vx4De8VOg9q+azczl5cwZckGPl+ynunLNvDajBVlr2nVMJtuLRvQvVUe3Vs1oHvLPA5pkBXVKtQ4CgURqVOy0lPp064xfdo1LptWsnUXM5eX8MXyEr5YtpEvlpfw1uxVZfsnmuZm0j0Iim4t8/hei1zaNKpHSkryDeinUBCROi+v3je7nSA2RtPsFRuZURQLi5nLNvL+3GL2nmCdnZ5K52b16dIsl8Oa5dKleey+WYPMOj36q0JBRJJS/cw0+rZvTN/2X29RbNu5my9XbmTuqk3MWbmZuas28f7cYp6fXFTWpkFWGoc1z42FRXDfpVkujeql14mwUCiIiASyM1JjRy+1bfSN6eu27GTuqk1BWMTuX5m2nKcmfj0seIOsNNo3yaFdfg7t8+t9475J/YxaExgKBRGR/Wick8FRHfM5qmN+2TR3Z9XGHcxZtYl5qzaxZN1WvlqzhWlLN/Da9OXEj/NXPzONdvn1aJ+fU3bfunE2rRpm0zwvi8y0mjPek0JBROQ7MDOa52XRPC+L73dp+o15O0v3sGzDNhat3cLiNVtYtHYri9ZuYdaKjbwxc2XZIbN7NamfSauGWbRsmE3Lhtm0yMuiVfC4ZcNs8nMyqm2nt0JBRKSKZaSl0KFJDh2a5MBh35xXujsWGMvWb2PZhm2sKNnO8g2xx3NXbeK9OcVs27X7m8tLTaFFwyx+enKXskNvw6JQEBGpRmmpKbTLj+17SMTdKdm2KxYYG7azvGRb2eP8nPDP0FYoiIjUIGZGw3oZNKyXEcnlSzV6lIiIlFEoiIhIGYWCiIiUUSiIiEgZhYKIiJRRKIiISBmFgoiIlFEoiIhImVp3jWYzKwYWf8eXNwHWVGE5tU0yr38yrzsk9/pr3WPauXvTfTWGWhgKB8PMCitz4eq6KpnXP5nXHZJ7/bXuB7bu6j4SEZEyCgURESmTbKEwIuoCIpbM65/M6w7Jvf5a9wOQVPsURERk35JtS0FERPZBoSAiImWSJhTM7FQzm2Nm883stqjrqU5mtsjMZpjZVDMrjLqesJnZKDNbbWZfxE1rbGZvmdm84L5RlDWGpYJ1v8PMlgWf/1QzOz3KGsNiZm3M7F0zm21mM83s5mB6snz2Fa3/AX3+SbFPwcxSgbnAyUARMAkY7O6zIi2smpjZIqDA3ZPiBB4zOx7YDDzh7t2DafcA69z9j8GPgkbu/oso6wxDBet+B7DZ3f8cZW1hM7MWQAt3/9zMcoHJwNnAFSTHZ1/R+l/AAXz+ybKl0A+Y7+4L3X0nMA4YGHFNEhJ3/wBYV27yQGBs8Hgssf8sdU4F654U3H2Fu38ePN4EzAZakTyffUXrf0CSJRRaAUvjnhfxHf5YtZgDb5rZZDO7JupiItLM3VdA7D8PcEjE9VS3YWY2PeheqpPdJ/HMrD3QG5hIEn725dYfDuDzT5ZQsATT6n6/2deOdfcjgdOAG4IuBkkefwU6Ab2AFcC90ZYTLjOrD7wA3OLuG6Oup7olWP8D+vyTJRSKgDZxz1sDyyOqpdq5+/LgfjXwErHutGSzKuhz3dv3ujrieqqNu69y993uvgcYSR3+/M0sndgX4lPu/mIwOWk++0Trf6Cff7KEwiSgs5l1MLMMYBAwPuKaqoWZ5QQ7nTCzHOAU4It9v6pOGg9cHjy+HPhnhLVUq71fiIFzqKOfv5kZ8Dgw293vi5uVFJ99Ret/oJ9/Uhx9BBAchvUAkAqMcve7Iy6pWphZR2JbBwBpwNN1fd3N7BngBGLDBq8CbgdeBp4D2gJLgPPdvc7tkK1g3U8g1nXgwCLgJ3v72OsSMxsAfAjMAPYEk39FrF89GT77itZ/MAfw+SdNKIiIyP4lS/eRiIhUgkJBRETKKBRERKSMQkFERMooFEREpIxCQWoMM/skuG9vZhdV8bJ/lei9wmJmZ5vZb0Na9q/23+qAl3mEmY2p6uVK7aNDUqXGMbMTgP929zMO4DWp7r57H/M3u3v9qqivkvV8Apx1sCPTJlqvsNbFzN4Ghrj7kqpettQe2lKQGsPMNgcP/wgcF4z9fquZpZrZn8xsUjCo10+C9icE48c/TeyEHczs5WDgv5l7B/8zsz8C2cHynop/L4v5k5l9YbFrTlwYt+z3zOx5M/vSzJ4KzhjFzP5oZrOCWr41HLGZdQF27A0EMxtjZn8zsw/NbK6ZnRFMr/R6xS070bpcYmafBdMeDYaKx8w2m9ndZjbNzCaYWbNg+vnB+k4zsw/iFv8KsbP9JZm5u2661YgbsTHfIXYG7qtx068BfhM8zgQKgQ5Buy1Ah7i2jYP7bGKn8+fHLzvBe50LvEXsTPdmxM54bREsu4TYOFkpwKfAAKAxMIevt7IbJliPK4F7456PAf4dLKczsbG4sg5kvRLVHjz+HrEv8/Tg+SPAZcFjB84MHt8T914zgFbl6weOBV6J+t+BbtHe0iobHiIROgXoYWbnBc/ziH257gQ+c/ev4treZGbnBI/bBO3W7mPZA4BnPNZFs8rM3gf6AhuDZRcBmNlUoD0wAdgOPGZmrwGvJlhmC6C43LTnPDYg2TwzWwgcfoDrVZH/AvoAk4INmWy+HvBtZ1x9k4ldZArgY2CMmT0HvPj1olgNtKzEe0odplCQ2sCAG939jW9MjO172FLu+UnA0e6+1czeI/aLfH/LrsiOuMe7gTR3LzWzfsS+jAcBw4ATy71uG7Ev+Hjld945lVyv/TBgrLv/MsG8Xe6+9313E/x/d/drzaw/8CNgqpn1cve1xP5W2yr5vlJHaZ+C1ESbgNy4528A1wXDAmNmXYIRX8vLA9YHgXA4cFTcvF17X1/OB8CFQf9+U+B44LOKCrPYWPV57v46cAuxgcbKmw0cWm7a+WaWYmadgI7EuqAqu17lxa/LO8B5ZnZIsIzGZtZuXy82s07uPtHdfwus4eth5btQR0dQlcrTloLURNOBUjObRqw//kFiXTefBzt7i0l8ScV/A9ea2XRiX7oT4uaNAKab2efufnHc9JeAo4FpxH69/9zdVwahkkgu8E8zyyL2K/3WBG0+AO41M4v7pT4HeJ/Yfotr3X27mT1WyfUq7xvrYma/IXZlvRRgF3ADsHgfr/+TmXUO6n8nWHeAHwCvVeL9pQ7TIakiITCzB4nttH07OP7/VXd/PuKyKmRmmcRCa4C7l0Zdj0RH3Uci4fg9UC/qIg5AW+A2BYJoS0FERMpoS0FERMooFEREpIxCQUREyigURESkjEJBRETK/H/zNZyyii/kFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_x = 12288\n",
    "n_h = 7\n",
    "n_y = 1\n",
    "layers_dims = (n_x,n_h,n_y)\n",
    "\n",
    "parameters = two_layer_model(train_x, train_set_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True,isPlot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(X, y, parameters):\n",
    "    \"\"\"\n",
    "    该函数用于预测L层神经网络的结果，当然也包含两层\n",
    "    \n",
    "    参数：\n",
    "     X - 测试集\n",
    "     y - 标签\n",
    "     parameters - 训练模型的参数\n",
    "    \n",
    "    返回：\n",
    "     p - 给定数据集X的预测\n",
    "    \"\"\"\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    n = len(parameters) // 2 # 神经网络的层数\n",
    "    p = np.zeros((1,m))\n",
    "    \n",
    "    #根据参数前向传播\n",
    "    probas, caches = L_model_forward(X, parameters)\n",
    "    \n",
    "    for i in range(0, probas.shape[1]):\n",
    "        if probas[0,i] > 0.5:\n",
    "            p[0,i] = 1\n",
    "        else:\n",
    "            p[0,i] = 0\n",
    "    \n",
    "    print(\"准确度为: \"  + str(float(np.sum((p == y))/m)))\n",
    "        \n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确度为: 1.0\n",
      "准确度为: 0.72\n"
     ]
    }
   ],
   "source": [
    "predictions_train = predict(train_x, train_y, parameters) #训练集\n",
    "predictions_test = predict(test_x, test_y, parameters) #测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L_layer_model(X, Y, layers_dims, learning_rate=0.0075, num_iterations=3000, print_cost=False,isPlot=True):\n",
    "    \"\"\"\n",
    "    实现一个L层神经网络：[LINEAR-> RELU] *（L-1） - > LINEAR-> SIGMOID。\n",
    "    \n",
    "    参数：\n",
    "\t    X - 输入的数据，维度为(n_x，例子数)\n",
    "        Y - 标签，向量，0为非猫，1为猫，维度为(1,数量)\n",
    "        layers_dims - 层数的向量，维度为(n_y,n_h,···,n_h,n_y)\n",
    "        learning_rate - 学习率\n",
    "        num_iterations - 迭代的次数\n",
    "        print_cost - 是否打印成本值，每100次打印一次\n",
    "        isPlot - 是否绘制出误差值的图谱\n",
    "    \n",
    "    返回：\n",
    "     parameters - 模型学习的参数。 然后他们可以用来预测。\n",
    "    \"\"\"\n",
    "    np.random.seed(1)\n",
    "    costs = []\n",
    "    \n",
    "    parameters = initialize_parameters_deep(layers_dims)\n",
    "    \n",
    "    for i in range(0,num_iterations):\n",
    "        AL , caches = L_model_forward(X,parameters)\n",
    "        \n",
    "        cost = compute_cost(AL,Y)\n",
    "        \n",
    "        grads = L_model_backward(AL,Y,caches)\n",
    "        \n",
    "        parameters = update_parameters(parameters,grads,learning_rate)\n",
    "        \n",
    "        #打印成本值，如果print_cost=False则忽略\n",
    "        if i % 100 == 0:\n",
    "            #记录成本\n",
    "            costs.append(cost)\n",
    "            #是否打印成本值\n",
    "            if print_cost:\n",
    "                print(\"第\", i ,\"次迭代，成本值为：\" ,np.squeeze(cost))\n",
    "    #迭代完成，根据条件绘制图\n",
    "    if isPlot:\n",
    "        plt.plot(np.squeeze(costs))\n",
    "        plt.ylabel('cost')\n",
    "        plt.xlabel('iterations (per tens)')\n",
    "        plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "        plt.show()\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataset():\n",
    "    train_dataset = h5py.File('/home/xieyipeng/Documents/MachineLearning/wuenda_py/couse1/do_not_src/week_4_2/datasets/train_catvnoncat.h5', \"r\")\n",
    "    train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n",
    "    train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n",
    "\n",
    "    test_dataset = h5py.File('/home/xieyipeng/Documents/MachineLearning/wuenda_py/couse1/do_not_src/week_4_2/datasets/test_catvnoncat.h5', \"r\")\n",
    "    test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n",
    "    test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n",
    "\n",
    "    classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n",
    "    \n",
    "    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n",
    "    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n",
    "    \n",
    "    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\n",
    "\n",
    "\n",
    "train_set_x_orig , train_set_y , test_set_x_orig , test_set_y , classes = load_dataset()\n",
    "\n",
    "train_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T \n",
    "test_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T\n",
    "\n",
    "train_x = train_x_flatten / 255\n",
    "train_y = train_set_y\n",
    "test_x = test_x_flatten / 255\n",
    "test_y = test_set_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0 次迭代，成本值为： 0.715731513413713\n",
      "第 100 次迭代，成本值为： 0.6747377593469114\n",
      "第 200 次迭代，成本值为： 0.6603365433622127\n",
      "第 300 次迭代，成本值为： 0.6462887802148751\n",
      "第 400 次迭代，成本值为： 0.6298131216927773\n",
      "第 500 次迭代，成本值为： 0.606005622926534\n",
      "第 600 次迭代，成本值为： 0.5690041263975135\n",
      "第 700 次迭代，成本值为： 0.519796535043806\n",
      "第 800 次迭代，成本值为： 0.46415716786282285\n",
      "第 900 次迭代，成本值为： 0.40842030048298916\n",
      "第 1000 次迭代，成本值为： 0.37315499216069037\n",
      "第 1100 次迭代，成本值为： 0.3057237457304712\n",
      "第 1200 次迭代，成本值为： 0.26810152847740837\n",
      "第 1300 次迭代，成本值为： 0.2387247482767261\n",
      "第 1400 次迭代，成本值为： 0.20632263257914712\n",
      "第 1500 次迭代，成本值为： 0.17943886927493558\n",
      "第 1600 次迭代，成本值为： 0.15798735818801332\n",
      "第 1700 次迭代，成本值为： 0.1424041301227409\n",
      "第 1800 次迭代，成本值为： 0.1286516599788666\n",
      "第 1900 次迭代，成本值为： 0.11244314998158064\n",
      "第 2000 次迭代，成本值为： 0.0850563103497123\n",
      "第 2100 次迭代，成本值为： 0.05758391198608928\n",
      "第 2200 次迭代，成本值为： 0.04456753454695382\n",
      "第 2300 次迭代，成本值为： 0.03808275166598416\n",
      "第 2400 次迭代，成本值为： 0.034410749018407925\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "layers_dims = [12288, 20, 7, 5, 1] #  5-layer model\n",
    "parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 2500, print_cost = True,isPlot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确度为: 0.9952153110047847\n",
      "准确度为: 0.78\n"
     ]
    }
   ],
   "source": [
    "pred_train = predict(train_x, train_y, parameters) #训练集\n",
    "pred_test = predict(test_x, test_y, parameters) #测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x2880 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def print_mislabeled_images(classes, X, y, p):\n",
    "    \"\"\"\n",
    "\t绘制预测和实际不同的图像。\n",
    "\t    X - 数据集\n",
    "\t    y - 实际的标签\n",
    "\t    p - 预测\n",
    "    \"\"\"\n",
    "    a = p + y\n",
    "    mislabeled_indices = np.asarray(np.where(a == 1))\n",
    "    plt.rcParams['figure.figsize'] = (40.0, 40.0) # set default size of plots\n",
    "    num_images = len(mislabeled_indices[0])\n",
    "    for i in range(num_images):\n",
    "        index = mislabeled_indices[1][i]\n",
    "        \n",
    "        plt.subplot(2, num_images, i + 1)\n",
    "        plt.imshow(X[:,index].reshape(64,64,3), interpolation='nearest')\n",
    "        plt.axis('off')\n",
    "        plt.title(\"Prediction: \" + classes[int(p[0,index])].decode(\"utf-8\") + \" \\n Class: \" + classes[y[0,index]].decode(\"utf-8\"))\n",
    "\n",
    "\n",
    "print_mislabeled_images(classes, test_x, test_y, pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确度为: 1.0\n",
      "y = 1.0, your L-layer model predicts a \"cat\"picture.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import imageio\n",
    "# import scipy.misc\n",
    "# image = '/home/xieyipeng/Pictures/timg.jpeg'\n",
    "\n",
    "#自己找图片来识别\n",
    "from PIL import Image\n",
    "my_label_y = [1] # the true class of your image (1 -> cat, 0 -> non-cat)\n",
    " \n",
    "num_px = 64\n",
    "image = Image.open('/home/xieyipeng/Pictures/timg (1).jpeg')\n",
    "\n",
    "my_image = np.array(image.resize((num_px,num_px),Image.ANTIALIAS))\n",
    "plt.imshow(my_image)\n",
    "my_image = my_image.reshape(num_px*num_px*3 , -1)\n",
    " \n",
    "predict_my_image = predict(my_image , my_label_y ,parameters)\n",
    "# plt.imshow(image)\n",
    "\n",
    " \n",
    "print(\"y = \" + str(np.squeeze(predict_my_image)) + \", your L-layer model predicts a \\\"\" + classes[int(np.squeeze(predict_my_image))].decode(\"utf-8\") + \"\\\"picture.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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